Vetted AWS Glue Professionals

“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”

JYOTHI N

Screened ReferencesStrong rec.

Senior Data Scientist specializing in analytics, experimentation, and BI on AWS

Austin, TX7y exp

“Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.”

“Backend/data platform engineer with production experience at Walmart and Molina Healthcare, building Python microservices on AWS (EKS + Lambda) for real-time inventory and recommendation systems. Strong in reliability/observability and incident leadership, plus modernizing legacy healthcare workflows and building resilient AWS Glue/PySpark pipelines with schema evolution and data quality controls.”

“QA automation engineer with American Express experience owning an end-to-end UI regression suite for critical payment/transaction workflows. Rebuilt the suite with Playwright (BDD/TestNG/POM) and integrated it into CI to catch release-blocking issues like UI/backend payment mismatches and session timeout defects, and applies risk-based test strategy including MFA payment flows.”

“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”

“Analytics professional with experience across healthcare and fintech, including building SQL/Python data pipelines at Optum and owning a fraud detection initiative at Razorpay. Stands out for combining messy-data cleanup, reproducible analytics workflows, and stakeholder-driven metric design, with a reported 25% improvement in fraud detection while keeping false positives under control.”

“Backend/platform engineer who owned high-volume Java/Spring Boot microservices on AWS (Kafka + RDS/DynamoDB) and has hands-on experience debugging complex production latency incidents across DB, JVM/GC, and async consumers. Also shipped applied AI features for ops, including an LLM-powered log analysis assistant and an incident-response agent with strong safety guardrails (schema-validated tool use, retries/backoff, and human-in-the-loop escalation).”

Aigo Madakimova

Screened

Senior Data Analyst specializing in audit analytics, automation, and financial data platforms

Malvern, PA6y exp

“Full-stack engineer with strong Next.js App Router + TypeScript experience who built and owned a production internal analytics dashboard end-to-end, including server-component data fetching, route handlers for secure proxying, and post-launch monitoring/caching fixes. Also designed Postgres data models and performance-tuned analytics queries, and built reliable BullMQ/Redis-based order-fulfillment workflows with idempotency, retries, and compensating refunds—comfortable operating with high ownership in early-stage teams.”